Computer Engineering Commons^™

Data Mining Of Unstructured Textual Information In Transportation Safety Domain: Exploring Methods, Opportunities And Limitations, Keneth Morgan Kwayu

Dissertations

The unprecedented increase in volume and influx of structured and unstructured data has overwhelmed conventional data management system capabilities in organizing, analyzing, and procuring useful information in a timely fashion. Structured data sources have a pre-defined pattern that makes data preprocessing and information retrieval tasks relatively easy for the current technologies that have been designed to handle structured and repeatable data. Unlike structured data, unstructured data usually exists in an unorganized format that offers no or little insight unless indexed and stored in an organized fashion. The inherent format of unstructured data exacerbates difficulties in data preprocessing and information extraction. …

Towards Enabling Unmanned Aerial Vehicles As A Service For Heterogeneous Applications, Farid Nait-Abdesselam, Ahmad Alsharoa, Mohamed Y. Selim, Daji Qiao, Ahmed E. Kamal

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing use of unmanned aerial vehicles (UAVs) in various commercial applications, such as precision agriculture and aerial remote sensing, is fast contributing to a significant growth in the UAV market. Also, it is crucial to provide continuous coverage after failures of wireless network components or additional bandwidth in high traffic situations. By introducing the concept of UAVs as a service (UaaS), we propose a novel framework, dubbed D3S, consisting of four phases: Demand, decision, deployment, and service. The main objective of this framework is to provide a realistic and streamlined approach to support the implementation of the UaaS paradigm. …

Towards Adaptive, Self-Configuring Networked Unmanned Aerial Vehicles, Nirupama Bulusu, Ehsan Aryafar, Feng Liu

Computer Science Faculty Publications and Presentations

Networked drones have the potential to transform various applications domains; yet their adoption particularly in indoor and forest environments has been stymied by the lack of accurate maps and autonomous navigation abilities in the absence of GPS, the lack of highly reliable, energy-efficient wireless communications, and the challenges of visually inferring and understanding an environment with resource-limited individual drones. We advocate a novel vision for the research community in the development of distributed, localized algorithms that enable the networked drones to dynamically coordinate to perform adaptive beam forming to achieve high capacity directional aerial communications, and collaborative machine learning to …

Enhance Portable Radiograph For Fast And High Accurate Covid-19 Monitoring, Ngan Le, James Sorensen, Toan Bui, Arabinda Choudhary, Khoa Luu, Hien Nguyen

Computer Science and Computer Engineering Faculty Publications and Presentations

This work aimed to assist physicians by improving their speed and diagnostic accuracy when interpreting portable CXRs as well as monitoring the treatment process to see whether a patient is improving or deteriorating with treatment. These objectives are in especially high demand in the setting of the ongoing COVID-19 pandemic. With the recent progress in the development of artificial intelligence (AI), we introduce new deep learning frameworks to align and enhance the quality of portable CXRs to be more consistent, and to more closely match higher quality conventional CXRs. These enhanced portable CXRs can then help the doctors provide faster …

A Study Of Implementation Methodologies For Distributed Real Time Collaboration, Lauren A. Craft

Master's Theses

Collaboration drives our world and is almost unavoidable in the programming industry. From higher education to the top technological companies, people are working together to drive discovery and innovation. Software engineers must work with their peers to accomplish goals daily in their workplace. When working with others there are a variety of tools to choose from such as Google Docs, Google Colab and Overleaf. Each of the aforementioned collaborative tools utilizes the Operational Transform (OT) technique in order to implement their real time collaboration functionality. Operational transform is the technique seen amongst most if not all major collaborative tools in …

Inventory Locating With Quuppa: The Design And Development Of A Real-Time Process Monitoring Web Application Solution, Dylan C. Moreland, Trevor J. Howell, John W. Takiff, Patrick S. Dillon, Theo E. Fritz, William K. Mcintyre

Industrial and Manufacturing Engineering

Viasat, Inc. requires precise inventory tracking at their production facility in San Diego, CA. Viasat has installed the Quuppa indoor real-time locating system (RTLS), which it uses to track the real-time position of high-value work-in-process items. In its current state, the system only displays in-the-moment location information, with no available functionality for storing historical data for review, analysis, or visualization. In addition, the data displayed is noisy and prone to significant random error. This paper provides an overview of RTLS methods and technologies, assesses alternative solutions to Viasat’s issue, demonstrates our RTLS integrated web app solution, analyzes its impact, and …

Automating Deep-Sea Video Annotation, Hanson Egbert

Master's Theses

As the world explores opportunities to develop offshore renewable energy capacity, there will be a growing need for pre-construction biological surveys and post-construction monitoring in the challenging marine environment. Underwater video is a powerful tool to facilitate such surveys, but the interpretation of the imagery is costly and time-consuming. Emerging technologies have improved automated analysis of underwater video, but these technologies are not yet accurate or accessible enough for widespread adoption in the scientific community or industries that might benefit from these tools.

To address these challenges, prior research developed a website that allows to: (1) Quickly play and annotate …

Agent-Based Semantic Role Mining For Intelligent Access Control In Multi-Domain Collaborative Applications Of Smart Cities, Rubina Ghazal, Ahmad Kamran Malik, Basit Raza, Nauman Qadeer, Nafees Qamar, Sajal Bhatia

School of Computer Science & Engineering Faculty Publications

Significance and popularity of Role-Based Access Control (RBAC) is inevitable; however, its application is highly challenging in multi-domain collaborative smart city environments. The reason is its limitations in adapting the dynamically changing information of users, tasks, access policies and resources in such applications. It also does not incorporate semantically meaningful business roles, which could have a diverse impact upon access decisions in such multi-domain collaborative business environments. We propose an Intelligent Role-based Access Control (I-RBAC) model that uses intelligent software agents for achieving intelligent access control in such highly dynamic multi-domain environments. The novelty of this model lies in using …

Dependencyvis: Helping Developers Visualize Software Dependency Information, Nathan Lui

Master's Theses

The use of dependencies have been increasing in popularity over the past decade, especially as package managers such as JavaScript's npm has made getting these packages a simple command to run. However, while incidents such as the left-pad incident has increased awareness of how vulnerable relying on these packages are, there is still some work to be done when it comes to getting developers to take the extra research step to determine if a package is up to standards. Finding metrics of different packages and comparing them is always a difficult and time consuming task, especially since potential vulnerabilities are …

Energy Efficient Computing Using Scalable General Purpose Analog Processors, Ethan Paul Palisoc De Guzman

Master's Theses

Due to fundamental physical limitations, conventional digital circuits have not been able to scale at the pace expected from Moore’s law. In addition, computationally intensive applications such as neural networks and computer vision demand large amounts of energy from digital circuits. As a result, energy efficient alternatives are needed in order to provide continued performance scaling. Analog circuits have many well known benefits: the ability to store more information onto a single wire and efficiently perform mathematical operations such as addition, subtraction, and differential equation solving. However, analog computing also comes with drawbacks such as its sensitivity to process variation …

A Power System-Based Iot Network For Remote Sensing Applications, Dominic Gaiero

Master's Theses

Cities around the world are facing increasingly significant challenges, including rapid urbanization, resource management, and environmental threats. In California for example, wildfires present an ever-growing threat that gravely harms people, destroys communities, and causes billions of dollars in damages. The task of addressing these environmental threats and many other challenges is greatly aided with widespread data collection and real-time inference. However, as IoT networks scale and require more energy for near-data analytics, the IoT endpoints require more power and complexity, limiting their deployment. Additionally, deploying endpoints in remote locations creates further challenges with higher reliability and communication constraints. In this …

Real-Time Machine Learning For Quickest Detection, Yongxin Liu

Doctoral Dissertations and Master's Theses

Safety-critical Cyber-Physical Systems (CPS) require real-time machine learning for control and decision making. One promising solution is to use deep learning to discover useful patterns for event detection from heterogeneous data. However, deep learning algorithms encounter challenges in CPS with assurability requirements: 1) Decision explainability, 2) Real-time and quickest event detection, and 3) Time-eficient incremental learning.

To address these obstacles, I developed a real-time Machine Learning Framework for Quickest Detection (MLQD). To be specific, I first propose the zero-bias neural network, which removes decision bias and preferabilities from regular neural networks and provides an interpretable decision process. Second, I discover …

Cyber Social Threats 2021: Ai, Covid-19 Vaccine, Detection And Countering Strategies, Ugur Kursuncu, Jeremy Blackburn, Yelena Mejova, Megan Squire, Amit Sheth

Publications

In recent years, online platforms have been utilized for promoting harmful content and behavior such as extremism, harassment, mis/disinformation, human trafficking, genderbased violence among others affecting our society, often leading to real-world events. Such content and behaviors are inherently complex, making the recognition of these narratives challenging for researchers as well as social media companies. The Cyber Social Threats (CySoc) Workshop 2021 aimed to facilitate a rich forum for researchers and practitioners from both academia and industry in the areas of computing and social science, to discuss novel avenues for research on interdisciplinary aspects of harmful communications on social media, …

Bibliometric Review On Iot Based System For Remote Downloading On Microcontroller, Arundhati Bandopadhyaya Ms., Diya Dodwad Ms., Diviyanshi Gupta Ms., Surya Koyyana Mr., Parag Narkhede Mr., Shripad Deshpande Mr.

Library Philosophy and Practice (e-journal)

Working with sensors has become an area of expertise within the domain of electronic engineering. On a day to day basis, thousands of sensors are put to use around us, for instance, in smoke alarms, speedometers, motors, computers, radiators etc. to accumulate sensor data, cables that connect the sensors to the bottom station after which the information is worked on. But cabling is often expensive especially when handling large scale industrial applications ^[5]. For an equivalent reason, low-cost wireless networks came into the picture recently and are in high demand. A research paper we found during our review of …

When Program Analysis Meets Bytecode Search: Targeted And Efficient Inter-Procedural Analysis Of Modern Android Apps In Backdroid, Daoyuan Wu, Debin Gao, Robert H. Deng, Rocky Chang

Research Collection School Of Computing and Information Systems

Widely-used Android static program analysis tools,e.g., Amandroid and FlowDroid, perform the whole-app interprocedural analysis that is comprehensive but fundamentallydifficult to handle modern (large) apps. The average app size hasincreased three to four times over five years. In this paper, weexplore a new paradigm of targeted inter-procedural analysis thatcan skip irrelevant code and focus only on the flows of securitysensitive sink APIs. To this end, we propose a technique calledon-the-fly bytecode search, which searches the disassembled appbytecode text just in time when a caller needs to be located. In thisway, it guides targeted (and backward) inter-procedural analysisstep by step until reaching …

Requirements Metrics - A Working List, William L. Honig

Computer Science: Faculty Publications and Other Works

A working set of metrics for review of requirements materials including documents.

Hierarchical Scheduling For Real-Time Periodic Tasks In Symmetric Multiprocessing, Tom Springer, Peiyi Zhao

Engineering Faculty Articles and Research

In this paper, we present a new hierarchical scheduling framework for periodic tasks in symmetric multiprocessor (SMP) platforms. Partitioned and global scheduling are the two main approaches used by SMP based systems where global scheduling is recommended for overall performance and partitioned scheduling is recommended for hard real-time performance. Our approach combines both the global and partitioned approaches of traditional SMP-based schedulers to provide hard real-time performance guarantees for critical tasks and improved response times for soft real-time tasks. Implemented as part of VxWorks, the results are confirmed using a real-time benchmark application, where response times were improved for soft …

Online Laboratory Course Using Low Tech Supplies To Introduce Digital Logic Design Concepts, Dhanya Nair

Engineering Faculty Articles and Research

This paper describes a Digital Logic Design Laboratory Course developed to engage students with hardware systems within an online setting. This is a junior level core course for students from Computer Science (CS), Computer Engineering (CE) and Electrical Engineering (EE). Hence, the laboratories are designed to provide the hands-on experience of breadboarding, testing and debugging essential to CE and EE while accommodating CS students with no prior hardware experience. Commercially available low-cost electronic trainers (portable workstations) are loaned to the students in addition to basic electronic components. To ensure a strong foundation in debugging, prior to utilizing these workstations, students …

Applying Deep Learning On Financial Sentiment Analysis, Cuiyuan Wang

Dissertations, Theses, and Capstone Projects

Portfolio Investment has always been appealing to investors and researchers. In the past, people tend to use historical trading information of the securities to predict the return or manage the portfolio. Nowadays, the literature has been proved that the market sentiment could predict asset prices. Specifically, it has been shown that the stock market movement is related to financial news and social media events. Thus, it becomes necessary to extract the sentiment of the financial news. We explicitly introduce the application of dictionary methods, traditional machine learning models and deep learning models on text classification. The experiment results show that …

Efficient Protocols For Multi-Party Computation, Tahereh Jafarikhah

Dissertations, Theses, and Capstone Projects

Secure Multi-Party Computation (MPC) allows a group of parties to compute a join function on their inputs without revealing any information beyond the result of the computation. We demonstrate secure function evaluation protocols for branching programs, where the communication complexity is linear in the size of the inputs, and polynomial in the security parameter. Our result is based on the circular security of the Paillier's encryption scheme. Our work followed the breakthrough results by Boyle et al. [9; 11]. They presented a Homomorphic Secret Sharing scheme which allows the non-interactive computation of Branching Programs over shares of the secret inputs. …